Due to increasing rates of adoption of electric vehicles (EVs), there is a strong need to deploy the necessary charging station infrastructure, together with routing strategies to manage traffic flow and congestion. This study addresses the location-routing problem (LRP) for a general EV charging system with stochastic charging requests regarding their locations, arrival times and charging times. The objective is to develop an efficient routing strategy of EVs to charging stations, as well as to determine the optimal charging station locations so as to minimize the demand’s mean response time. Under some regularity assumptions on the mean waiting time at each charging station (e.g. system operates in a light or heavy traffic regime), we show that the optimization problem can be formulated as a partition-based clustering problem with size constraints. This relaxation of the problem formulation enables us to develop a novel data-driven approach for solving the charging station LRP, without requiring detailed stochastic models for the EV’s charging requests, as well as the queueing behavior of the charging stations. An algorithm along with two size adjustment strategies are developed to solve the obtained clustering problem and illustrated on urban areas of Seattle with various types of distance, vehicle speeds, distributions for charging request locations, and inter-arrival time densities.